





from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel





@require_tf
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   BlenderbotSmallConfig
  __lowerCAmelCase   :						Any							=   {}
  __lowerCAmelCase   :						str							=   """gelu"""


  def __init__(							self ,__snake_case ,__snake_case=1_3 ,__snake_case=7 ,__snake_case=True ,__snake_case=False ,__snake_case=9_9 ,__snake_case=3_2 ,__snake_case=2 ,__snake_case=4 ,__snake_case=3_7 ,__snake_case=0.1 ,__snake_case=0.1 ,__snake_case=2_0 ,__snake_case=2 ,__snake_case=1 ,__snake_case=0 ,):



       """simple docstring"""





       A_          =       parent
       A_          =       batch_size
       A_          =       seq_length
       A_          =       is_training
       A_          =       use_labels
       A_          =       vocab_size
       A_          =       hidden_size
       A_          =       num_hidden_layers
       A_          =       num_attention_heads
       A_          =       intermediate_size

       A_          =       hidden_dropout_prob
       A_          =       attention_probs_dropout_prob
       A_          =       max_position_embeddings
       A_          =       eos_token_id
       A_          =       pad_token_id
       A_          =       bos_token_id


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
       A_          =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
       A_          =       tf.concat([input_ids, eos_tensor] ,axis=1 )

       A_          =       ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )

       A_          =       self.config_cls(
           vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
       A_          =       prepare_blenderbot_small_inputs_dict(__snake_case ,__snake_case ,__snake_case )
       return config, inputs_dict




  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       A_          =       TFBlenderbotSmallModel(config=__snake_case ).get_decoder()
       A_          =       inputs_dict['''input_ids''']

       A_          =       input_ids[:1, :]
       A_          =       inputs_dict['''attention_mask'''][:1, :]
       A_          =       inputs_dict['''head_mask''']
       A_          =       1

       # first forward pass
       A_          =       model(__snake_case ,attention_mask=__snake_case ,head_mask=__snake_case ,use_cache=__snake_case )

       A_   ,    A_          =       outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       A_          =       ids_tensor((self.batch_size, 3) ,config.vocab_size )
       A_          =       tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )

       # append to next input_ids and
       A_          =       tf.concat([input_ids, next_tokens] ,axis=-1 )
       A_          =       tf.concat([attention_mask, next_attn_mask] ,axis=-1 )

       A_          =       model(__snake_case ,attention_mask=__snake_case )[0]
       A_          =       model(__snake_case ,attention_mask=__snake_case ,past_key_values=__snake_case )[0]

       self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )

       # select random slice
       A_          =       int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
       A_          =       output_from_no_past[:, -3:, random_slice_idx]
       A_          =       output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(__snake_case ,__snake_case ,rtol=1E-3 )




def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:Optional[Any]  ,						_UpperCAmelCase							:Tuple=None  ,						_UpperCAmelCase							:Union[str, Any]=None  ,						_UpperCAmelCase							:Dict=None  ,						_UpperCAmelCase							:List[Any]=None  ,						_UpperCAmelCase							:int=None  ,						)	->				int:
     '''simple docstring'''


     if attention_mask is None:
          A_          =       tf.cast(tf.math.not_equal(UpperCamelCase_  ,						config.pad_token_id				)  ,						tf.inta				)
     if decoder_attention_mask is None:
          A_          =       tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape  ,						dtype=tf.inta				),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,						config.pad_token_id				)  ,						tf.inta				),
              ]  ,						axis=-1  ,						)
     if head_mask is None:
          A_          =       tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
     if decoder_head_mask is None:
          A_          =       tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     if cross_attn_head_mask is None:
          A_          =       tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }





@require_tf
class UpperCAmelCase_				( snake_case__     ,		snake_case__     ,		unittest.TestCase ):
  __lowerCAmelCase   :						int							=   (
      (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
  )
  __lowerCAmelCase   :						str							=   (TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
  __lowerCAmelCase   :						List[Any]							=   (
      {
          """conversational""": TFBlenderbotSmallForConditionalGeneration,
          """feature-extraction""": TFBlenderbotSmallModel,
          """summarization""": TFBlenderbotSmallForConditionalGeneration,
          """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
          """translation""": TFBlenderbotSmallForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  __lowerCAmelCase   :						Tuple							=   True
  __lowerCAmelCase   :						Dict							=   False
  __lowerCAmelCase   :						List[str]							=   False


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFBlenderbotSmallModelTester(self )
       A_          =       ConfigTester(self ,config_class=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       self.config_tester.run_common_tests()




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__snake_case )





@require_tokenizers
@require_tf
class UpperCAmelCase_				( unittest.TestCase ):
  __lowerCAmelCase   :						int							=   [
      """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
      """  i\'m going to throw up.\nand why is that?"""
  ]
  __lowerCAmelCase   :						str							=   """facebook/blenderbot_small-90M"""


  @cached_property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )


  @cached_property
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
       return model




  @slow
  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.tokenizer(self.src_text ,return_tensors='''tf''' )
       A_          =       self.model.generate(
           model_inputs.input_ids ,attention_mask=model_inputs.attention_mask ,num_beams=2 ,use_cache=__snake_case ,)
       A_          =       self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=__snake_case )[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )







import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Any      =       logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE     :					str      =       {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_SCREAMING_SNAKE_CASE     :					Any      =       {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "allenai/longformer-base-4096": 40_96,
    "allenai/longformer-large-4096": 40_96,
    "allenai/longformer-large-4096-finetuned-triviaqa": 40_96,
    "allenai/longformer-base-4096-extra.pos.embd.only": 40_96,
    "allenai/longformer-large-4096-extra.pos.embd.only": 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							(
         list(range(ord('''!'''							)				,ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,ord('''ÿ'''							) + 1							)							)
     )
     snake_case											=							bs[:]
     snake_case											=							0
     for b in range(2**8							):
          if b not in bs:
               bs.append(UpperCamelCase_							)
               cs.append(2**8 + n							)
               n += 1
     snake_case											=							[chr(UpperCamelCase_							) for n in cs]
     return dict(zip(UpperCamelCase_				,UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							set()
     snake_case											=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          snake_case											=							char
     return pairs

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__         =						['input_ids', 'attention_mask']
    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case="replace"     ,   __snake_case="<s>"     ,   __snake_case="</s>"     ,   __snake_case="</s>"     ,   __snake_case="<s>"     ,   __snake_case="<unk>"     ,   __snake_case="<pad>"     ,   __snake_case="<mask>"     ,   __snake_case=False     ,   **__snake_case     ,   ):
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else bos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else eos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else sep_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else cls_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else unk_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else mask_token

         super().__init__(
             errors=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   unk_token=__snake_case     ,   sep_token=__snake_case     ,   cls_token=__snake_case     ,   pad_token=__snake_case     ,   mask_token=__snake_case     ,   add_prefix_space=__snake_case     ,   **__snake_case     ,   )

         with open(__snake_case     ,   encoding='''utf-8'''       ) as vocab_handle:
              snake_case											=							json.load(__snake_case       )
         snake_case											=							{v: k for k, v in self.encoder.items()}
         snake_case											=							errors  # how to handle errors in decoding
         snake_case											=							bytes_to_unicode()
         snake_case											=							{v: k for k, v in self.byte_encoder.items()}
         with open(__snake_case     ,   encoding='''utf-8'''       ) as merges_handle:
              snake_case											=							merges_handle.read().split('''\n'''       )[1:-1]
         snake_case											=							[tuple(merge.split()       ) for merge in bpe_merges]
         snake_case											=							dict(zip(__snake_case     ,   range(len(__snake_case       )       )       )       )
         snake_case											=							{}
         snake_case											=							add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case											=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
    @property
    def 						a_     (			self       ):
         return len(self.encoder       )
    def 						a_     (			self       ):
         return dict(self.encoder     ,   **self.added_tokens_encoder       )
    def 						a_     (			self     ,   __snake_case       ):
         if token in self.cache:
              return self.cache[token]
         snake_case											=							tuple(__snake_case       )
         snake_case											=							get_pairs(__snake_case       )

         if not pairs:
              return token

         while True:
              snake_case											=							min(__snake_case     ,   key=lambda __snake_case       : self.bpe_ranks.get(__snake_case     ,   float('''inf'''       )       )       )
              if bigram not in self.bpe_ranks:
                   break
              snake_case					,    snake_case											=							bigram
              snake_case											=							[]
              snake_case											=							0
              while i < len(__snake_case       ):
                   try:
                        snake_case											=							word.index(__snake_case     ,   __snake_case       )
                   except ValueError:
                        new_word.extend(word[i:]       )
                        break
                   else:
                        new_word.extend(word[i:j]       )
                        snake_case											=							j

                   if word[i] == first and i < len(__snake_case       ) - 1 and word[i + 1] == second:
                        new_word.append(first + second       )
                        i += 2
                   else:
                        new_word.append(word[i]       )
                        i += 1
              snake_case											=							tuple(__snake_case       )
              snake_case											=							new_word
              if len(__snake_case       ) == 1:
                   break
              else:
                   snake_case											=							get_pairs(__snake_case       )
         snake_case											=							''' '''.join(__snake_case       )
         snake_case											=							word
         return word
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							[]
         for token in re.findall(self.pat     ,   __snake_case       ):
              snake_case											=							''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case       ).split(''' '''       )       )
         return bpe_tokens
    def 						a_     (			self     ,   __snake_case       ):
         return self.encoder.get(__snake_case     ,   self.encoder.get(self.unk_token       )       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.decoder.get(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							''''''.join(__snake_case       )
         snake_case											=							bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     ,   errors=self.errors       )
         return text
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if not os.path.isdir(__snake_case       ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
              return
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as f:
              f.write(json.dumps(self.encoder     ,   indent=2     ,   sort_keys=__snake_case     ,   ensure_ascii=__snake_case       ) + '''\n'''       )

         snake_case											=							0
         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as writer:
              writer.write('''#version: 0.2\n'''       )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,   key=lambda __snake_case       : kv[1]       ):
                   if index != token_index:
                        logger.warning(
                            F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                            ''' Please check that the tokenizer is not corrupted!'''       )
                        snake_case											=							token_index
                   writer.write(''' '''.join(__snake_case       ) + '''\n'''       )
                   index += 1

         return vocab_file, merge_file
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case											=							[self.cls_token_id]
         snake_case											=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = False       ):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__snake_case     ,   token_ids_a=__snake_case     ,   already_has_special_tokens=__snake_case       )

         if token_ids_a is None:
              return [1] + ([0] * len(__snake_case       )) + [1]
         return [1] + ([0] * len(__snake_case       )) + [1, 1] + ([0] * len(__snake_case       )) + [1]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False     ,   **__snake_case       ):
         snake_case											=							kwargs.pop('''add_prefix_space'''     ,   self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(__snake_case       ) > 0 and not text[0].isspace()):
              snake_case											=							''' ''' + text
         return (text, kwargs)




